Year: 2025
Type: article
Abstract: Mobile Edge computing (MEC) enables network functions and control programmable and operates key constituents of social networks in terms of increasing user’s support on devices to carry out compute. It requires traffic offloading and task scheduling to improve the storage and fast computing. In this paper, a novel method, including data driven traffic modeling enabled by a Reinforcement learning algorithm (RLTOA), is proposed for offloading traffic and improving the computing speed and minimizing the application latency of the social network. The result of the proposed data driven modeling is compared with existing methods and validate how the data driven traffic modeling for providing the computation offloading service in terms of energy budget and the mobile drop and execution of edge server. The presented computation offloading, and energy management solutions can provide valuable perceptions for practical applications of MEC. Extensive numerical findings are presented to endorse the efficacy of RLTOA and display the effect of the social network requirement.
Authors: Nandhini Jembu Mohanram, Saravanan Kaliaperumal, Anuratha Kesavan, Uma Sankar
Institution: Saveetha University
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $215
OpenAlex ID: W4414240215
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